Amazon EMR is ideal for Hadoop-based processing
The AWS stack is a big component of the majority of our work. When necessary, EMR is employed in a number of these settings. When we need …
Overview
What is Amazon EMR?
Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability…
Recent Reviews
Awards
Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards
Product Details
- About
- Tech Details
- FAQs
What is Amazon EMR?
Amazon EMR Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Frequently Asked Questions
Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.
Reviewers rate Support Rating highest, with a score of 9.
The most common users of Amazon EMR are from Small Businesses (1-50 employees).
Comparisons
Compare with
Reviews and Ratings
(60)Attribute Ratings
Reviews
(1-5 of 5)Companies can't remove reviews or game the system. Here's why
April 22, 2022
Amazon EMR is ideal for Hadoop-based processing
The AWS stack is a big component of the majority of our work. When necessary, EMR is employed in a number of these settings. When we need to process a large amount of data across several EC2 servers, our DevOps team implements it. For our customers, EMR is attractive since it is far less expensive to adopt than alternative solutions, which means that the overall cost savings are substantial.
- Faster than prior on-premise systems to put in place.
- Open source software is supported.
- Reduces the cost of production.
- Automation of processing jobs creation and deletion.
- The cost of this service is more expensive than similar ones.
- Getting everything up and running at the beginning is a lengthy process.
- Compatibility between Spark and Hive workloads in Hadoop.
- EC2 Cluster Monitoring and Logging is at least five times slower than our data processing.
- Cost reductions for consumers are enormous as compared to other (more conventional) choices, particularly on-premise ones.
- S3 and EC2 API connection allowed for increased scalability.
Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
April 08, 2022
Amazon EMR - It's very intuitive and easy to use.
We migrated the entire hadoop structure to Amazon EMR, the cost and maintenance are much better compared to other solutions on the market. We created a recommender system filter in big data. We needed a low runtime to meet our demand and we were able to get through the Amazon EMR.We migrated the entire hadoop structure to Amazon EMR, the cost and maintenance are much better compared to other solutions on the market. We have a lot of data science tasks, like calculating statistics between various math calculations to apply the business rules. Definitely one of the best services to work on bigdata.
- Faster processing.
- The distributed computation of the calculations.
- Easy to setup.
- Monitoring as an add up.
- Can be integrated with lots of technologies.
- Overhead delay in starting clusters which can cause problems.
- Configuration simplify
- Spark
- We use Hadoop and Spark frameworks. To process and store big data
Good choice for startup, open source and cost-effective and saves a lot of setup time.
Run times are reduced to minutes compared to hours on EC2 or other compute servers.
Easy to choose between hadoop or spark based EMR cluster, it can be used in combination with other AWS services, for example we can create budgets involving EMR and various other tasks in AWS data pipeline service.
Run times are reduced to minutes compared to hours on EC2 or other compute servers.
Easy to choose between hadoop or spark based EMR cluster, it can be used in combination with other AWS services, for example we can create budgets involving EMR and various other tasks in AWS data pipeline service.
September 20, 2020
AWS's EMR: the best option out there
We are a certified AWS partner agency, and we use a lot of the AWS stack for most of our projects. EMR is used in several of them when required. It is implemented by our DevOps team and we pretty much use it when we need to process a lot of data throughout EC2 instances. EMR is very compelling to our customers because it is easier to implement (hence less dev cost) and it is way more efficient when managing the data VS other tools, so the overall cost reduction is considerable.
- Easier to implement than older on-premise solutions
- Works with open source technologies.
- Keeps processing cost low.
- It is flexible and works also for short term workloads and the pricing changes to that model.
- You definitely need to be trained before using it because the interface can be a little confusing. It is a professional service model, so I recommend a certified dev.
- As an agency, we have won new customers just by having these EMR capabilities as a service.
- For the customers, the savings on costs are huge when compared with ore (more traditional) options, especially with on-premise ones.
EMR is more suited for developers. Databricks feel more for data science-oriented with its notebooks and customs visualizations. With EMR you can more easily add additional capacity on-damnd on the instance. With others is a more cumbersome process. And then, you can also configure it to be dynamic and change depending on your usual flow of data.
October 25, 2017
AWS EMR at a glance!!
We have used AWS EMR before starting to use Databricks on EC2 instances. EMR was solving the problem but we needed a better solution (Enterprise edition) to manage our Workbooks and better scheduler for running or jobs. EMR was working fine but we did not find it user friendly to add the data nodes on demand. We used EMR primarily to process the data on AWS S3 using Hadoop and Spark frameworks. We have also used AWS SWF to orchestrate our job flow by adding steps. It was used widely by the data processing team and not by the entire organization as most of the data was on local servers. It addresses problems like processing data which might not need to be processed live as the cluster can be spun up and shut down once the job is completed. It is cost efficient (especially if you do not need data nodes and only task nodes), scalable and reliable.
- EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources.
- EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy).
- EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great.
- Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
- Like some of the enterprise editions EMR does not provide on premises options.
- No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.
- It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
- It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
- It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.
Having one of these enterprise edition license comes at its own costs. But, the flexibility to have the cluster spin up with the workbenches and code snippets on the same is really beneficial. Especially, if one had to move out of EMR and consider an option which reduces the debugging time in establishing connections to AWS resources, I would love to used the mentioned three resources on EC2. This would definitely make the processing time to reduce as there is a flexibility to test real time and execute the code snippet and look at the performance and monitor the snippet in real time.
June 22, 2016
Research Experience with Amazon EMR
As a PhD student, I used Amazon Elastic MapReduce for my research for analyzing my data. Firstly, it was very scalable and did not cause much performance impact when using large data sets. Secondly, their web console is very easy to use and intuitive. There were many resources that could be used whenever I encountered any problems with EMR.
- The cluster size of MapReduce is very dynamic and therefore scalability is good for EMR.
- It also works well with other Amazon Web Services like Amazon Simple Storage Service, which means that data can be taken from those services and written back to them.
- I tried using the in-house hosting at the university I work in, but there would be a lot of complications with technical support required. For Amazon, the support and documentation was good to solve these problems faster.
- It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases.
- Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR.
- If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3.
- Positive: Helped process the jobs amazingly fast.
- Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time.
- Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house.
- Cloudera
EMR provides dynamic cluster size, lots of documentation, and integration with other Amazon Web Services which are some of the things that Cloudera distribution for Hadoop lacked. Some products are hard to learn but EMR was much easier and helped save time spent on trying to figure out how to deploy projects in MapReduce.